CALL FOR BOOK CHAPTERS - Science Publishers and CRC Press


BOOK TITLE: Genome Mapping and Advancements in Deep Learning Approaches: Computation and Analysis in Medical Applications


This book is a valuable resource at the point of genome mapping and deep learning, providing a thorough examination of innovative approaches that are revolutionizing medical analysis. It delves into cutting-edge ways to bridge the gap between genetics, computational biology, and machine learning, delivering a unified narrative for both experts and students. . In the present era of intelligent data analysis, the high-performance organizations face four challenges. They are data analysis, data growth, different data formats and less time for the decision-making. In any organization, data is an important asset. To store the data prevailing in any kind of company, digitalization helps providing inexpensive data storage devices. It will help to store all related transaction data in the form of large information system. The Current business world, the data bases can range in size into the tera bytes or peta bytes of data. This is an indication of tremendous data growth, and this comes with a difficulty in terms of retrieving the hidden and useful information, which can be used for decision making. This opens opportunities for a unique technique, which will work effectively to retrieve the hidden and useful decision-making information even during data growth in the databases.

Topics (Proposed Tentative Chapters)

  •  Part 1: Genome Mapping using Machine Learning: Genome Mapping: Need of Big data, Issues and challenges in Big data, Traditional and modern technique using Genome Mapping, Approaches/Technologies used for Big data
  •  Part 2.  Introduction to Neural Network and machine learning as subset of AI: Introduction of AI which is superset of ML. Reviewing   of   the   fundamental   concepts   of   AI and ML with respect to medical science, Machine learning versus Neural Network, Image data, dataset and their importance in machine learning
  • Part 3 Machine Learning Models in medical science: Discusses the glaring issues in medical science which can be solved with machine learning, Image Fundamentals and classification, Machine Learning Models, Optimization Methods and Regularization, Efficient Image parsing in medical science, Methodology, Experiments
  •  Part 4: Multi Instance and multistage machine learning for medical image and text data recognition: Related Work, Methodology, Results and Analysis
  • Part 5: Deep feature representation learning provides a scalable, high-performance medical fields framework: Proposed method, Experiments
  • Part 6. Image-based medical problem diagnosis using machine learning: Machine Learning techniques and applications that can be used to develop intelligent, long-term solutions for overall well-being in the future.
  • Part 7 Parallel Programming Using Compute Unified Device Architecture (CUDA) and real time Big data: Problem definition of CUDA, Medical Image Model, Regression Strategy, Feature Extraction,  CUDA, Experiments and results
  •  Part 8: GPU Computing for large scale Big data text and image analysis using machine learning: Fundamental of  GPU, GPU architecture, Parallel Computing using GPU
  •  Part 9: Medical text, image data and identifying disease prediction: Case study
  • .

Authors are invited to submit their full chapter (strictly follow the submission deadline date: 31/05/2024) to the below email id:                
NB: There are no submission or acceptance fees for manuscripts submitted to this book publication. All manuscripts are accepted based on a double-blind peer-review editorial process

Post a Comment

Previous Post Next Post